Assessing three perfect prognosis methods for statistical downscaling of climate change precipitation scenarios

Under the perfect prognosis approach, statistical downscaling methods learn the relationships between large-scale variables from reanalysis and local observational records. These relationships are subsequently applied to downscale future global climate model (GCM) simulations in order to obtain projections for the local region and variables of interest. However, the capability of such methods to produce future climate change signals consistent with those from the GCM, often referred to as transferability, is an important issue that remains to be carefully analyzed. Using the EC-Earth GCM and focusing on precipitation, we assess the transferability of generalized linear models, convolutional neural networks and a posteriori random forests (APRFs). We conclude that APRFs present the best overall performance for the historical period, and future local climate change signals consistent with those projected by EC-Earth. Moreover, we show how a slight modification of APRFs can greatly improve the temporal consistency of the downscaled seriesThis study is part of the R&D project “Eventos extremos compuestos para la evaluación de los impactos del cambio climático en la agricultura" (COMPOUND: TED2021-131334A-I00) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. R. Manzanas acknowledges support from the R&D project "Contribución a la nueva generación de proyecciones climáticas regionales de CORDEX mediante técnicas dinámicas y estadísticas" (CORDyS: PID2020-116595RB-I00). M. Vrac and S. Thao acknowledge support from the H2020 funded project XAIDA with the Grant Agreement number 101003469, and from the COESION project funded by the French National program LEFE (Les Enveloppes Fluides et l’Environnement). Additionally, M. N. Legasa acknowledges partial funding by the French embassy in Spain (“Convocatoria de proyectos científicos de la Embajada de Francia en España para el año 2022”)